Implementasi Algoritma Convolutional Neural Network untuk Pendeteksi Objek dalam Rumah pada Mata Rabun

Authors

  • Pramadika Egamo Universitas Teknologi Yogyakarta
  • Arief Hermawan Universitas Teknologi Yogyakarta

DOI:

https://doi.org/10.33020/saintekom.v13i2.456

Keywords:

Convolutional Neural Network, Yolov5, accuracy, object in the house, myopia eyes

Abstract

The increased use of laptops and smartphones during the COVID-19 pandemic has led to an increase in the number of people suffering from nearsightedness. Convolutional Neural Network (CNN) is a class of deep learning that is capable of recognizing images and classifying images. Convolutional Neural Network is a technique inspired by the way mammals (humans) generate vision. CNN can be used to help nearsighted people detect or see objects in the house. With the CNN algorithm, this algorithm will be implemented to detect objects in the house to help people with myopic eyes. The number of epochs is varied in the dataset training process using Yolov5 which is included in the Convolutional Neural Network algorithm. The training dataset results show that the highest accuracy is 95%, which is obtained through mAp (mean Average Precision) calculation. The training process was carried out using a batch size of 16 and running training for 100 epochs. Different from previous research, this research implements the CNN algorithm to detect objects in the house to help people with nearsighted eyes.

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References

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Published

30-09-2023

How to Cite

Egamo, Pramadika, and Arief Hermawan. 2023. “Implementasi Algoritma Convolutional Neural Network Untuk Pendeteksi Objek Dalam Rumah Pada Mata Rabun”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 13 (2):173-83. https://doi.org/10.33020/saintekom.v13i2.456.

Issue

Vol. 13 No. 2 (2023): September 2023

Section

Articles

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Copyright (c) 2023 Pramadika Egamo, Arief Hermawan

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